Welcome to Data Handling 2022!

  • Go to this page (or use the QR code): https://bit.ly/datahandling-2022
  • Use one row to respond to the questions in the column headers (see the first two rows for examples).

Background

‘Data Science’?

“This coupling of scientific discovery and practice involves the collection, management, processing, analysis, visualization, and interpretation of vast amounts of heterogeneous data associated with a diverse array of scientific, translational, and inter-disciplinary applications.”

University of Michigan ‘Data Science Initiative’, 2015

But, what about statistics?!

“Seemingly, statistics is being marginalized here; the implicit message is that statistics is a part of what goes on in data science but not a very big part. At the same time, many of the concrete descriptions of what the DSI will actually do will seem to statisticians to be bread-and-butter statistics. Statistics is apparently the word that dare not speak its name in connection with such an initiative!”

David Donoho (2015). 50 years of Data Science

What’s new about all this?

“All in all, I have come to feel that my central interest is in data analysis, which I take to include, among other things: …”

What’s new about all this?

“All in all, I have come to feel that my central interest is in data analysis, which I take to include, among other things: procedures for analyzing data, techniques for interpreting the results of such procedures, ways of planning the gathering of data to make its analysis easier, more precise or more accurate, and all the machinery and results of (mathematical) statistics which apply to analyzing data.”

What’s new about all this?

John Tukey (The Future of Data Analysis, 1962!)

Technological change

Relevance for modern economic research

Relevance for modern economic research

Relevance for modern economic research

Relevance for modern economic research

Data science in economics skill set

Organisation of the Course

Our Team - At Your Service

Aurélien Sallin Michael Tüting Ulrich Matter

Introduction: Ulrich Matter

  • 2017-today: Assistant Professor of Economics, University of St.Gallen
  • 2021-today: Guest Lecturer (Applied Data Science), University of Lucerne

Previously:

Introduction: Ulrich Matter

Research:

  • Can the personalization of Google search results lead to political polarization?
  • Does YouTube’s recommender algorithm lead to radicalization?
  • Do politicians vote in the interest of money donors when voters are distracted?

Introduction: Ulrich Matter

Teaching:

  • Well, this course…
  • Big Data Analytics (Master)
  • Introduction to Web Mining (Master)
  • Economics in Practice (Master)

Course Structure

Course concept: lectures

  • Lectures (Thursday morning)
    • Background/Concepts
    • Illustration concepts
    • Illustration of ‘hands-on’ approaches

Course concept: special lectures

  • 27.10.2022: Research insights
    • Ulrich Matter: Web Data
    • Aurélien Sallin: Text as Data
    • Michael Tüting: Images as Data

24/11/2022: Guest lecture: Economic Data Science, SNB

Dr. Matthias Gubler Dr. Helge Liebert
Head of Economic Data Science, SNB
Swiss National Bank Economist, SNB

Course concept: exercises

  • Exercise sheets (handed out every other week)
    • Some conceptual questions
    • Hands-on exercises/tutorials in R
    • Detailed solution videos
    • First Exercises (set up R/RStudio) is available on StudyNet/Canvas today

Course concept

  • Learning mode in this course: Prepare with reading, visit the lecture, recap key concepts in lecture notes (self-study), work on exercises, watch solution video, come to exercise session, repeat…

  • Strongly encouraged: (virtual) learning groups!

    • Biweekly exercises provide opportunity.
    • Tackle the tricky exercises together!

Course concept: exercise sessions

  • In-class exercise sessions (bi-weekly evening sessions)
    • Discussion of exercises and additional input
    • Recap of concepts
    • Q&A, support
    • time for more coding!

Part I: Data (Science) fundamentals

Date Topic
22.09.2022 Introduction: Big Data/Data Science, course overview
29.09.2022 Programming with R
29.09.2022 Exercises/Workshop 1: Tools, programming
06.10.2022 An introduction to data and data processing
13.10.2022 Data storage and data structures
13.10.2022 Exercises/Workshop 2: Data storage and data structures
20.10.2022 Web data, text, and images
27.10.2022 Research insights
27.10.2022 Exercises/Workshop 3: Web data, text, and images

Part II: Data gathering and preparation

Date Topic
17.11.2022 Data sources, data gathering, data import
24.11.2022 Guest Lecture
24.11.2022 Exercises/Workshop 4: Data gathering, data import
01.12.2022 Data preparation and manipulation

Part III: Analysis, visualisation, output

Date Topic
08.12.2022 Basic statistics and data analysis with R
08.12.2022 Exercises/Workshop 5: Data preparation and applied data analysis with R
15.12.2022 Visualisation, dynamic documents
21.12.2022 Exercises/Workshop 6: Visualization, dynamic documents
22.12.2021 Summary, Wrap-Up, Q&A, Feedback
22.12.2021 Exam for Exchange Students

Core course resources

  • All information and materials (notes, slides, course sheet, syllabus, etc.) are available on StudyNet/Canvas.
  • Core materials will also be made available on Nuvolos.

Main textbooks

Further resources

Exam information

  • Central, written examination: digital, BYOD!, we will have an instructional session by the head of the digital examinations team (data TBD).
  • Multiple choice questions.
  • A few open questions.
  • Theoretical concepts and practical applications in R (questions based on code examples).

Exam information II

  • We will release samples of multiple choice questions via Quizzes on Canvas/Studynet (exact same format and style of exam questions).
  • Exchange students who need to take the exam before the central exam block:

And now this…

Q&A

References